In the addon of the session, we calculated the distances to the closest hospitals located within North-Rhine Westphalia (NRW). Still, we did not show how to subset the original file, which contains all hospitals in Germany.
Subset the data file yourself by relying on the spatial information of the filehospital_points.csv and a polygon of NRW only. How
many hospitals are located within the borders of NRW?
hospital_points.csv in the ./data folder and
polygons of NRW. For the latter, you can again use the
osmdata syntax.
The default of sf::st_join() will leave you with a
‘left-join’ and returns a data object with all hospitals and matching
district information for those which are located within NRW. You can
reset the option to perform an ‘inner-join’ and keep only the
observation which lay within the predefined area
(sf::st_join(x , y, join = "", left = FALSE)).
# load hospitals
hospitals <-
read.csv(
"./data/hospital_points.csv",
header = TRUE,
fill = TRUE,
sep = ","
) %>%
sf::st_as_sf(coords = c("X", "Y"), crs = 3035)
# use the OSM function
nrw <-
osmdata::getbb(
"Nordrhein-Westfalen",
format_out = "sf_polygon"
) %>%
.$multipolygon %>%
sf::st_transform(3035)
# spatial join
nrw_hospitals <-
hospitals %>%
sf::st_join(
# point layer nrw
nrw,
# chose intersect or within
join = sf::st_intersects,
# option FALSE will
# keep only the hospital
# which could be joined
left = FALSE
)
nrw_hospitals## Simple feature collection with 344 features and 4 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 4039950 ymin: 3058862 xmax: 4277224 ymax: 3246338
## Projected CRS: ETRS89-extended / LAEA Europe
## First 10 features:
## name
## 319 Ev. Krankenhaus
## 320 Fliedner Klinik Ambulanz und Tagesklinik f\xfcr Psychiatrie, Psychotherapie und Psychosomatik
## 321 Johanniter-Tagesklinik Klinik f\xfcr Psychiatrie und Psychotherapie
## 322 Krankenanstalten Florence Nightingale
## 323 Krankenhaus M\xf6rsenbroich-Rath
## 324 LVR-Klinikum D\xfcsseldorf Klinikum der Heinrich-Heine- Universit\xe4t D\xfcsseldorf
## 325 Marien-Hospital
## 326 Medical Center D\xfcsseldorf -Luisenkrankenhaus-
## 327 Paracelsus Klinik Golzheim Fachklinik f\xfcr Urologie und Kinderurologie
## 328 Sana Kliniken D\xfcsseldorf
## year beds ags geometry
## 319 2017 568 5111000 POINT (4095599 3127379)
## 320 2017 . 5111000 POINT (4096285 3128719)
## 321 2017 . 5111000 POINT (4101713 3121737)
## 322 2017 568 5111000 POINT (4094348 3137304)
## 323 2017 718 5111000 POINT (4099466 3133197)
## 324 2017 495 5111000 POINT (4100738 3130402)
## 325 2017 461 5111000 POINT (4096526 3129923)
## 326 2017 42 5111000 POINT (4099035 3129691)
## 327 2017 81 5111000 POINT (4094841 3132032)
## 328 2017 490 5111000 POINT (4101452 3130494)# 344 hospitals in NRWDid the operationalization of health care provision convince you? Don’t you think it might be more important how many hospitals are close to survey respondents? To test this, we want to calculate the number of hospitals (and/or hospital beds) per district in North-Rhine Westphalia.
dplyr::as_tibble() data frame to use the
functions dplyr::group_by() and
dplyr::summarise().
dplyr::n() allows summarizing the total count
of hospitals. sum(beds) for summarizing the bed total per
district.
nrw_districts <-
sf::read_sf("./data/VG250_KRS.shp") %>%
sf::st_transform(3035) %>%
sf::st_join(nrw, join = sf::st_intersects, left = FALSE)
nrw_hospitals <-
nrw_hospitals %>%
# beds were character, now numeric
dplyr::mutate(beds = as.numeric(beds)) %>%
# replace NAs as zeros for simplification
replace(is.na(.), 0)
district_hospital_join <-
nrw_hospitals %>%
# join the hospitals
# within districts
sf::st_join(nrw_districts, join = sf::st_within) %>%
# use as tibble to perform
# group by & summarise
dplyr::as_tibble() %>%
dplyr::group_by(AGS) %>%
dplyr::summarise(
hospital_count = dplyr::n(),
hospital_bed_count = sum(as.numeric(beds))
) %>%
# left join the new information
# to the original data frame
dplyr::left_join(nrw_districts, .)## Joining with `by = join_by(AGS)`district_hospital_join ## Simple feature collection with 73 features and 25 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 4031313 ymin: 2972671 xmax: 4332216 ymax: 3337853
## Projected CRS: ETRS89-extended / LAEA Europe
## # A tibble: 73 × 26
## ADE GF BSG ARS AGS SDV_ARS GEN BEZ IBZ BEM NBD SN_L
## <int> <int> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
## 1 4 4 1 03155 03155 031550011011 Nort… Land… 43 -- ja 03
## 2 4 4 1 03251 03251 032510012012 Diep… Land… 43 -- ja 03
## 3 4 4 1 03252 03252 032520006006 Hame… Land… 43 -- ja 03
## 4 4 4 1 03255 03255 032550023023 Holz… Land… 43 -- ja 03
## 5 4 4 1 03256 03256 032560022022 Nien… Land… 43 -- ja 03
## 6 4 4 1 03257 03257 032570035035 Scha… Land… 43 -- ja 03
## 7 4 4 1 03404 03404 034040000000 Osna… Krei… 40 -- ja 03
## 8 4 4 1 03454 03454 034540035035 Emsl… Land… 43 -- ja 03
## 9 4 4 1 03456 03456 034560015015 Graf… Land… 43 -- ja 03
## 10 4 4 1 03459 03459 034040000000 Osna… Land… 43 -- ja 03
## # ℹ 63 more rows
## # ℹ 14 more variables: SN_R <chr>, SN_K <chr>, SN_V1 <chr>, SN_V2 <chr>,
## # SN_G <chr>, FK_S3 <chr>, NUTS <chr>, ARS_0 <chr>, AGS_0 <chr>, WSK <date>,
## # DEBKG_ID <chr>, geometry <MULTIPOLYGON [m]>, hospital_count <int>,
## # hospital_bed_count <dbl>